Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi Selangor 43600, Malaysia.
Sensors (Basel). 2013 May 17;13(5):6605-35. doi: 10.3390/s130506605.
Biosignal analysis is one of the most important topics that researchers have tried to develop during the last century to understand numerous human diseases. Electroencephalograms (EEGs) are one of the techniques which provides an electrical representation of biosignals that reflect changes in the activity of the human brain. Monitoring the levels of anesthesia is a very important subject, which has been proposed to avoid both patient awareness caused by inadequate dosage of anesthetic drugs and excessive use of anesthesia during surgery. This article reviews the bases of these techniques and their development within the last decades and provides a synopsis of the relevant methodologies and algorithms that are used to analyze EEG signals. In addition, it aims to present some of the physiological background of the EEG signal, developments in EEG signal processing, and the effective methods used to remove various types of noise. This review will hopefully increase efforts to develop methods that use EEG signals for determining and classifying the depth of anesthesia with a high data rate to produce a flexible and reliable detection device.
生物信号分析是上个世纪研究人员试图开发的最重要的课题之一,旨在理解许多人类疾病。脑电图(EEG)是提供生物信号电表示的技术之一,反映了人脑活动的变化。监测麻醉水平是一个非常重要的课题,它旨在避免由于麻醉药物剂量不足导致的患者意识和手术期间麻醉过度使用。本文综述了这些技术的基础及其在过去几十年中的发展,并提供了用于分析脑电图信号的相关方法和算法的概述。此外,它旨在介绍脑电图信号的一些生理背景、脑电图信号处理的发展以及用于去除各种类型噪声的有效方法。希望这篇综述能够增加使用脑电图信号来确定和分类麻醉深度的方法的开发力度,以实现高数据速率、灵活和可靠的检测设备。